Consecutive measurement of structures formed on a semiconductor wafer using a polarized reflectometer

ABSTRACT

Structures formed on a semiconductor wafer are consecutively measured by obtaining first and second measured diffraction signals of a first structure and a second structure formed abutting the first structure. The first and second measured diffraction signals were consecutively measured using a polarized reflectometer. The first measured diffraction signal is compared to a first simulated diffraction signal generated using a profile model of the first structure. The profile model has profile parameters that characterize geometries of the first structure. One or more features of the first structure are determined based on the comparison. The second measured diffraction signal is converted to a converted diffraction signal. The converted diffraction signal is compared to the first simulated diffraction signal or a second simulated diffraction signal generated using the same profile model as the first simulated diffraction signal. One or more features of the second structure are determined based on the comparison.

BACKGROUND

1. Field

The present application generally relates to optical metrology of astructure formed on a semiconductor wafer, and, more particularly toconsecutive measurement of structures formed on a semiconductor waferusing a polarized reflectometer.

2. Related Art

In semiconductor manufacturing, periodic gratings are typically used forquality assurance. For example, one typical use of periodic gratingsincludes fabricating a periodic grating in proximity to the operatingstructure of a semiconductor chip. The periodic grating is thenilluminated with an electromagnetic radiation. The electromagneticradiation that deflects off of the periodic grating are collected as adiffraction signal. The diffraction signal is then analyzed to determinewhether the periodic grating, and by extension whether the operatingstructure of the semiconductor chip, has been fabricated according tospecifications.

In one conventional optical metrology system, the diffraction signalcollected from illuminating the periodic grating (themeasured-diffraction signal) is compared to a library ofsimulated-diffraction signals. Each simulated-diffraction signal in thelibrary is associated with a hypothetical profile. When a match is madebetween the measured-diffraction signal and one of thesimulated-diffraction signals in the library, the hypothetical profileassociated with the simulated-diffraction signal is presumed torepresent the actual profile of the periodic grating.

The library of simulated-diffraction signals can be generated using arigorous method, such as rigorous coupled wave analysis (RCWA). Moreparticularly, in the diffraction modeling technique, asimulated-diffraction signal is calculated based, in part, on solvingMaxwell's equations. Calculating the simulated diffraction signalinvolves performing a large number of complex calculations, which can betime consuming and costly. Typically, a number of optical metrologymeasurements are performed for a number of sites in a wafer. The numberof wafers that can be processed in a time period is proportional to thespeed of determining the structure profile from the measured diffractionsignals.

SUMMARY

In one exemplary embodiment, structures formed on a semiconductor waferare consecutively measured by obtaining first and second measureddiffraction signals of a first structure and a second structure formedabutting the first structure. The first and second measured diffractionsignals were consecutively measured using a polarized reflectometer. Thefirst measured diffraction signal is compared to a first simulateddiffraction signal generated using a profile model of the firststructure. The profile model has profile parameters that characterizegeometries of the first structure. One or more features of the firststructure are determined based on the comparison. The second measureddiffraction signal is converted to a converted diffraction signal. Theconverted diffraction signal is compared to the first simulateddiffraction signal or a second simulated diffraction signal generatedusing the same profile model as the first simulated diffraction signal.One or more features of the second structure are determined based on thecomparison.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A is an architectural diagram illustrating an exemplary embodimentwhere optical metrology can be utilized to determine the profiles ofstructures on a semiconductor wafer.

FIG. 1B depicts an exemplary one-dimension repeating structure.

FIG. 1C depicts an exemplary two-dimension repeating structure.

FIGS. 2A-2E depict various exemplary profile models.

FIG. 3A depicts exemplary orthogonal grid of unit cells of atwo-dimension repeating structure.

FIG. 3B depicts a top-view of a two-dimension repeating structure.

FIG. 3C is an exemplary technique for characterizing the top-view of atwo-dimension repeating structure.

FIG. 4A, 4B, and 4C are exemplary architectural diagram of the top-viewof measurement structures for consecutive measurements of diffractionsignals using reflectometric measurements.

FIG. 5 is an exemplary flowchart for an integrated optical metrologyprocess of using a profile model.

FIG. 6 is an exemplary architectural diagram for linking one or morepairs of fabrication systems with a metrology processor.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENT(S)

In order to facilitate the description of the present invention, asemiconductor wafer may be utilized to illustrate an application of theconcept. The methods and processes equally apply to other work piecesthat have repeating structures. Furthermore, in this application, theterm structure when it is not qualified refers to a patterned structure.

1. Optical Metrology Tools

With reference to FIG. 1A, an optical metrology system 100 can be usedto examine and analyze a structure formed on a semiconductor wafer 104.For example, optical metrology system 100 can be used to determine oneor more features of a periodic grating 102 formed on wafer 104. Asdescribed earlier, periodic grating 102 can be formed in a test pad onwafer 104, such as adjacent to a die formed on wafer 104. Periodicgrating 102 can be formed in a scribe line and/or an area of the diethat does not interfere with the operation of the die.

As depicted in FIG. 1A, optical metrology system 100 can include aphotometric device with a source 106 and a detector 112. Periodicgrating 102 is illuminated by an illumination beam 108 from source 106.The illumination beam 108 is directed onto periodic grating 102 at anangle of incidence θ_(i) with respect to normal {right arrow over (n)}of periodic grating 102 and an azimuth angle Φ (i.e., the angle betweenthe plane of incidence beam 108 and the direction of the periodicity ofperiodic grating 102). Diffracted beam 110 leaves at an angle of θ_(d)with respect to normal and is received by detector 112. Detector 112converts the diffracted beam 110 into a measured diffraction signal,which can include reflectance, tan (Ψ), cos(Δ), Fourier coefficients,and the like. Although a zero-order diffraction signal is depicted inFIG. 1A, it should be recognized that non-zero orders can also be used.For example, see Ausschnitt, Christopher P., “A New Approach to PatternMetrology,” Proc. SPIE 5375-7, Feb. 23, 2004, pp 1-15, which isincorporated herein by reference in its entirety.

Optical metrology system 100 also includes a processing module 114configured to receive the measured diffraction signal and analyze themeasured diffraction signal. Processing module 114 is configured todetermine one or more features of the periodic grating using any numberof methods which provide a best matching diffraction signal to themeasured diffraction signal. These methods are described below andinclude a library-based process or a regression based process usingsimulated diffraction signals obtained by rigorous coupled wave analysisand machine learning systems.

2. Library-Based Process of Determining Feature of Structure

In a library-based process of determining one or more features of astructure, the measured diffraction signal is compared to a library ofsimulated diffraction signals. More specifically, each simulateddiffraction signal in the library is associated with a hypotheticalprofile of the structure. When a match is made between the measureddiffraction signal and one of the simulated diffraction signals in thelibrary or when the difference of the measured diffraction signal andone of the simulated diffraction signals is within a preset or matchingcriterion, the hypothetical profile associated with the matchingsimulated diffraction signal is presumed to represent the actual profileof the structure. The matching simulated diffraction signal and/orhypothetical profile can then be utilized to determine whether thestructure has been fabricated according to specifications.

Thus, with reference again to FIG. 1A, in one exemplary embodiment,after obtaining a measured diffraction signal, processing module 114then compares the measured diffraction signal to simulated diffractionsignals stored in a library 116. Each simulated diffraction signal inlibrary 116 can be associated with a hypothetical profile. Thus, when amatch is made between the measured diffraction signal and one of thesimulated diffraction signals in library 116, the hypothetical profileassociated with the matching simulated diffraction signal can bepresumed to represent the actual profile of periodic grating 102.

The set of hypothetical profiles stored in library 116 can be generatedby characterizing the profile of periodic grating 102 using a profilemodel. The profile model is characterized using a set of profileparameters. The set of profile parameters of the profile model arevaried to generate hypothetical profiles of varying shapes anddimensions. The process of characterizing the actual profile of periodicgrating 102 using the profile model and a set of profile parameters canbe referred to as parameterizing.

For example, as depicted in FIG. 2A, assume that profile model 200 canbe characterized by profile parameters h1 and w1 that define its heightand width, respectively. As depicted in FIGS. 2B to 2E, additionalshapes and features of profile model 200 can be characterized byincreasing the number of profile parameters. For example, as depicted inFIG. 2B, profile model 200 can be characterized by profile parametersh1, w1, and w2 that define its height, bottom width, and top width,respectively. Note that the width of profile model 200 can be referredto as the critical dimension (CD). For example, in FIG. 2B, profileparameter w1 and w2 can be described as defining the bottom CD (BCD) andtop CD (TCD), respectively, of profile model 200.

As described above, the set of hypothetical profiles stored in library116 (FIG. 1A) can be generated by varying the profile parameters thatcharacterize the profile model. For example, with reference to FIG. 2B,by varying profile parameters h1, w1, and w2, hypothetical profiles ofvarying shapes and dimensions can be generated. Note that one, two, orall three profile parameters can be varied relative to one another.

With reference again to FIG. 1A, the number of hypothetical profiles andcorresponding simulated diffraction signals in the set of hypotheticalprofiles and simulated diffraction signals stored in library 116 (i.e;,the resolution and/or range of library 116) depends, in part, on therange over which the profile parameters and the increment at which theprofile parameters are varied. The hypothetical profiles and thesimulated diffraction signals stored in library 116 are generated priorto obtaining a measured diffraction signal from an actual structure.Thus, the range and increment (i.e., the range and resolution) used ingenerating library 116 can be selected based on familiarity with thefabrication process for a structure and what the range of variance islikely to be. The range and/or resolution of library 116 can also beselected based on empirical measures, such as measurements using AFM,X-SEM, and the like.

For a more detailed description of a library-based process, see U.S.patent application Ser. No. 09/907,488, titled GENERATION OF A LIBRARYOF PERIODIC GRATING DIFFRACTION SIGNALS, filed on Jul. 16, 2001, whichis incorporated herein by reference in its entirety.

3. Regression-Based Process of Determining Feature of Structure

In a regression-based process of determining one or more features of astructure, the measured diffraction signal is compared to a simulateddiffraction signal (i.e., a trial diffraction signal). The simulateddiffraction signal is generated prior to the comparison using a set ofprofile parameters (i.e., trial profile parameters) for a hypotheticalprofile. If the measured diffraction signal and the simulateddiffraction signal do not match or when the difference of the measureddiffraction signal and one of the simulated diffraction signals is notwithin a preset or matching criterion, another simulated diffractionsignal is generated using another set of profile parameters for anotherhypothetical profile, then the measured diffraction signal and the newlygenerated simulated diffraction signal are compared. When the measureddiffraction signal and the simulated diffraction signal match or whenthe difference of the measured diffraction signal and one of thesimulated diffraction signals is within a preset or matching criterion,the hypothetical profile associated with the matching simulateddiffraction signal is presumed to represent the actual profile of thestructure. The matching simulated diffraction signal and/or hypotheticalprofile can then be utilized to determine whether the structure has beenfabricated according to specifications.

Thus, with reference again to FIG. 1A, the processing module 114 cangenerate a simulated diffraction signal for a hypothetical profile, andthen compare the measured diffraction signal to the simulateddiffraction signal. As described above, if the measured diffractionsignal and the simulated diffraction signal do not match or when thedifference of the measured diffraction signal and one of the simulateddiffraction signals is not within a preset or matching criterion, thenprocessing module 114 can iteratively generate another simulateddiffraction signal for another hypothetical profile. The subsequentlygenerated simulated diffraction signal can be generated using anoptimization algorithm, such as global optimization techniques, whichincludes simulated annealing, and local optimization techniques, whichincludes steepest descent algorithm.

The simulated diffraction signals and hypothetical profiles can bestored in a library 116 (i.e., a dynamic library). The simulateddiffraction signals and hypothetical profiles stored in library 116 canthen be subsequently used in matching the measured diffraction signal.

For a more detailed description of a regression-based process, see U.S.patent application Ser. No. 09/923,578, titled METHOD AND SYSTEM OFDYNAMIC LEARNING THROUGH A REGRESSION-BASED LIBRARY GENERATION PROCESS,filed on Aug. 6, 2001, which is incorporated herein by reference in itsentirety.

4. Rigorous Coupled Wave Analysis

As described above, simulated diffraction signals are generated to becompared to measured diffraction signals. As will be described below,the simulated diffraction signals can be generated by applying Maxwell'sequations and using a numerical analysis technique to solve Maxwell'sequations. It should be noted, however, that various numerical analysistechniques, including variations of RCWA, can be used.

In general, RCWA involves dividing a hypothetical profile into a numberof sections, slices, or slabs (hereafter simply referred to assections). For each section of the hypothetical profile, a system ofcoupled differential equations is generated using a Fourier expansion ofMaxwell's equations (i.e., the components of the electromagnetic fieldand permittivity (ε)). The system of differential equations is thensolved using a diagonalization procedure that involves eigenvalue andeigenvector decomposition (i.e., Eigen-decomposition) of thecharacteristic matrix of the related differential equation system.Finally, the solutions for each section of the hypothetical profile arecoupled using a recursive-coupling schema, such as a scattering matrixapproach. For a description of a scattering matrix approach, see LifengLi, “Formulation and comparison of two recursive matrix algorithms formodeling layered diffraction gratings,” J. Opt. Soc. Am. A13, pp1024-1035 (1996), which is incorporated herein by reference in itsentirety. For a more detail description of RCWA, see U.S. patentapplication Ser. No. 09/770,997, titled CACHING OF INTRA-LAYERCALCULATIONS FOR RAPID RIGOROUS COUPLED-WAVE ANALYSES, filed on Jan. 25,2001, which is incorporated herein by reference in its entirety.

5. Machine Learning Systems

The simulated diffraction signals can be generated using a machinelearning system (MLS) employing a machine learning algorithm, such asback-propagation, radial basis function, support vector, kernelregression, and the like. For a more detailed description of machinelearning systems and algorithms, see “Neural Networks” by Simon Haykin,Prentice Hall, 1999, which is incorporated herein by reference in itsentirety. See also U.S. patent application Ser. No. 10/608,300, titledOPTICAL METROLOGY OF STRUCTURES FORMED ON SEMICONDUCTOR WAFERS USINGMACHINE LEARNING SYSTEMS, filed on Jun. 27, 2003, which is incorporatedherein by reference in its entirety.

In one exemplary embodiment, the simulated diffraction signals in alibrary of diffraction signals, such as library 116 (FIG. 1A), used in alibrary-based process are generated using a MLS. For example, a set ofhypothetical profiles can be provided as inputs to the MLS to produce aset of simulated diffraction signals as outputs from the MLS. The set ofhypothetical profiles and set of simulated diffraction signals arestored in the library.

In another exemplary embodiment, the simulated diffractions used inregression-based process are generated using a MLS, such as MLS 118(FIG. 1A). For example, an initial hypothetical profile can be providedas an input to the MLS to produce an initial simulated diffractionsignal as an output from the MLS. If the initial simulated diffractionsignal does not match the measured diffraction signal, anotherhypothetical profile can be provided as an additional input to the MLSto produce another simulated diffraction signal.

FIG. 1A depicts processing module 114 having both a library 116 and MLS118. It should be recognized, however, that processing module 114 canhave either library 116 or MLS 118 rather than both. For example, ifprocessing module 114 only uses a library-based process, MLS 118 can beomitted. Alternatively, if processing module 114 only uses aregression-based process, library 116 can be omitted. Note, however, aregression-based process can include storing hypothetical profiles andsimulated diffraction signals generated during the regression process ina library, such as library 116.

The term “one-dimension structure” is used herein to refer to astructure having a profile that varies only in one dimension. Forexample, FIG. 1B depicts a periodic grating having a profile that variesonly in one dimension (i.e., the x-direction). The profile of theperiodic grating depicted in FIG. 1B varies in the z-direction as afunction of the x-direction. However, the profile of the periodicgrating depicted in FIG. 1B is assumed to be substantially uniform orcontinuous in the y-direction.

The term “two-dimension structure” is used herein to refer to astructure having a profile that varies in two-dimensions. For example,FIG. 1C depicts a periodic grating having a profile that varies in twodimensions (i.e., the x-direction and the y-direction). The profile ofthe periodic grating depicted in FIG. 1C varies in the z-direction.

Discussion for FIGS. 3A, 3B, and 3C below describe the characterizationof two-dimension repeating structures for profile modeling. FIG. 3Adepicts a top-view of exemplary orthogonal grid of unit cells of atwo-dimension repeating structure. A hypothetical grid of lines issuperimposed on the top-view of the repeating structure where the linesof the grid are drawn along the direction of periodicity. Thehypothetical grid of lines forms areas referred to as unit cells. Theunit cells may be arranged in an orthogonal or non-orthogonalconfiguration. Two-dimension repeating structures may comprise featuressuch as repeating posts, contact holes, vias, islands, or combinationsof one or more pairs of shapes within a unit cell. Furthermore, thefeatures may have a variety of shapes and may be concave or convexfeatures or a combination of concave and convex features. Referring toFIG. 3A, the repeating structure 300 comprises unit cells with holesarranged in an orthogonal manner. Unit cell 302 includes all thefeatures and components inside the unit cell 302, primarily comprising ahole 304 substantially in the center of the unit cell 302.

FIG. 3B depicts a top-view of a two-dimension repeating structure. Unitcell 310 includes a concave elliptical hole. FIG. 3B shows a unit cell310 with a feature 320 that comprises an elliptical hole wherein thedimensions become progressively smaller until the bottom of the hole.Profile parameters used to characterize the structure includes theX-pitch 312 and the Y-pitch 314. In addition, the major axis of theellipse 316 that represents the top of the feature 320 and the majoraxis of the ellipse 318 that represents the bottom of the feature 320may be used to characterize the feature 320. Furthermore, anyintermediate major axis between the top and bottom of the feature mayalso be used as well as any minor axis of the top, intermediate, orbottom ellipse, (not shown).

FIG. 3C is an exemplary technique for characterizing the top-view of atwo-dimension repeating structure. A unit cell 330 of a repeatingstructure is a feature 332, an island with a peanut-shape viewed fromthe top. One modeling approach includes approximating the feature 332with a variable number or combinations of ellipses and polygons. Assumefurther that after analyzing the variability of the top-view shape ofthe feature 322, it was determined that two ellipses, Ellipsoid 1 andEllipsoid 2, and two polygons, Polygon 1 and Polygon 2 were found tofully characterize feature 332. In turn, parameters needed tocharacterize the two ellipses and two polygons comprise nine parametersas follows: T1 and T2 for Ellipsoid 1; T3, T4, and θ₁ for Polygon 1; T4,T5, and θ₂ for Polygon 2; T6 and T7 for Ellipsoid 2. Many othercombinations of shapes could be used to characterize the top-view of thefeature 332 in unit cell 330. For a detailed description of modelingtwo-dimension repeating structures, refer to U.S. patent applicationSer. No. 11/061,303, OPTICAL METROLOGY OPTIMIZATION FOR REPETITIVESTRUCTURES, by Vuong, et al., filed on Apr. 27, 2004, which isincorporated in its entirety herein by reference.

6. Consecutive Measurement

The typical sequence of steps for optical metrology measurements ofstructures include loading the wafer, positioning the optical metrologydevice to the measurement site by either moving the measurement head orthe wafer, alignment of the illumination beam to the measurementstructure, performing the measurement, and unloading the wafer. FIGS.4A, 4B, and 4C are exemplary architectural diagrams of the top-views ofmeasurement structures for consecutive measurements of diffractionsignals using reflectometric measurements. The arrangement of themeasurement structures depicted in FIGS. 4A, 4B, and 4C facilitatemeasuring the measurement structures consecutively, thereby eliminatingthe steps of loading and reloading the wafer for subsequentmeasurements.

Referring to FIG. 4A, the measurement structures 500 include a verticalline and space repeating structure 504 and a horizontal line and spacerepeating structure 508. Repeating structure 508 is formed abuttingrepeating structure 504. Repeating structure 508 is formed to have thesame features as repeating structure 504 but rotated about 90 degrees.Repeating structures 504 and 508 may be in different layers in amulti-layer structure, such as those in chemical-mechanicalplanarization (CMP) layers.

In the present exemplary embodiment, a polarized reflectometer is usedto measure diffraction signals of repeating structures 504, 508. A firstdiffraction signal can be measured of repeating structure 504 using thepolarized reflectometer. A second diffraction signal can be measured ofrepeating structure 508 using the polarized reflectometer. A typicalreflectometer at zero angle of incidence (AOI), i.e., normal AOI, may beused to measure repeating structures 504 and 508. However, it isunderstood that reflectometers with a non-normal incidence illuminationbeam may be used.

The first and second measured diffraction signals are measuredconsecutively. Thus, in the present exemplary embodiment, the secondmeasured diffraction signal is measured without unloading and reloadingthe semiconductor wafer after the first measured diffraction signal ismeasured. Also, the second measured diffraction signal is measuredwithout measuring another diffraction signal of another structure afterthe first measured diffraction signal is measured.

The first measured diffraction signal is compared to a first simulateddiffraction signal generated using a profile model of the firststructure. As described above, the profile model includes profileparameters that characterize geometries of the first structure.

One or more features of the first structure are determined based on thecomparison of the first measured diffraction signal to the firstsimulated diffraction signal. In particular, as described above, alibrary-based or regression-based process can be used to determine oneor more features of the first structure based on the comparison of thefirst measured diffraction signal to the first simulated diffractionsignal.

In the present exemplary embodiment, the second measured diffractionsignal is converted to a converted diffraction signal. The converteddiffraction signal can be calculated as the negative of the secondmeasured diffraction signal as shown below:

S₂=−(S₁)   1.01.

S₁ is the second measured diffraction signal. S₂ is the converteddiffraction signal.

The converted diffraction signal is compared to the first simulateddiffraction signal or a second simulated diffraction signal generatedusing the same profile model as the first simulated diffraction signal.In particular, if the converted diffraction signal and the firstsimulated diffraction signal do not match within a matching criterion,the converted diffraction signal can be compared to the second simulateddiffraction signal.

One or more features of the second structure are determined based on thecomparison of the converted diffraction signal to the first or secondsimulated diffraction signal. In particular, as described above, alibrary-based or regression-based process can be used to determine oneor more features of the first structure based on the comparison of theconverted diffraction signal to the first or second simulateddiffraction signal.

When a library-based process is used to determine the one or morefeatures of the first and second structures, the first and secondsimulated diffraction signals are obtained from a library of simulateddiffraction signals. In the present exemplary embodiment, by convertingthe second measured diffraction signal to a converted diffractionsignal, one library of simulated diffraction signals can be used for thecomparison of the first measured diffraction signal and the converteddiffraction signal. Thus, the simulated diffraction signals in thelibrary of simulated diffraction signals were generated using the sameprofile model for the first and second structures. As described above,to generate the simulated diffraction signals in the library, theprofile parameters of the profile model are varied to generate a set ofhypothetical profiles. The simulated diffraction signals are generatedusing the set of hypothetical profiles.

When a regression-based process is used to determine the one or morefeatures of the first and second structures, the first and secondsimulated diffraction signals are generated during the regressionprocess using the same profile model. In particular, a firsthypothetical profile is generated using a first setting of profileparameters of the profile model. The first simulated diffraction signalis generated using the first hypothetical profile. In determining one ormore features of the second structure, if the converted diffractionsignal and the first simulated diffraction signal do not match within amatching criterion, a second hypothetical profile is generated using asecond setting of profile parameters of the profile model, where thesecond setting of profile parameters is different than the firstsetting. The second simulated diffraction signal is generated using thesecond hypothetical profile. The converted diffraction signal is thencompared to the second simulated diffraction signal.

Referring to FIG. 4B, the measurement structures 520 comprises tworepeating structures where only one unit cell each 514 and 524 of therepeating structures are shown. Unit cell 514 depicts a top-view of anelliptical post 516 over an elliptical island 518. The major axis of theelliptical island 518 is larger than the major axis of the post 516 andthe major axis of the elliptical island 518 is about 45 degrees from avertical position. Unit cell 524 is similar to unit cell 514 except thatunit cell 524 is rotated 90 degrees clockwise compared to unit cell514B. As mentioned above, a polarized reflectometer is used to measurethe repeating structures of the measurement structures 520. One or morefeatures of measurement structures 520 can be determined as describedabove.

FIG. 4C depicts two pairs of measurement structures 540. Repeatingstructure 542 is similar to repeating structure 504 in FIG. 4A, i.e., arepeating structure of vertical lines and spaces. Repeating structure544 is similar to repeating structure 508 in FIG. 4A (i.e., a repeatingstructure of horizontal lines and spaces), and repeating structure 542is similar to repeating structure 504 in FIG. 4A (i.e., a repeatingstructure of vertical lines and spaces). Repeating structure 546 is arepeating structure of contact holes in orthogonal unit cells, where thecontact holes are in the shape of ellipses substantially located in thecenter of the orthogonal unit cells, with the major axis of the ellipsein a horizontal position. Repeating structure 548 is a repeatingstructure of contact holes in orthogonal unit cells, where the contactholes are also in the shape of ellipses substantially located in thecenter of the unit cells, with the major axis of the ellipse in avertical position. These measurement structures may be measuredconsecutively in any order. It should be understood that any one or morepairs of measurement structures that are fabricated can be consecutivelymeasured.

As mentioned above, one, two or more pairs of measurement structuresfabricated in contiguous areas in the wafer may be measured in aconsecutive manner using a sequencing method such as clockwise orcounter clockwise. However, it should be noted that any algorithm thatminimizes the positioning the optical metrology device to themeasurement site by either moving the measurement head or the wafer canbe used as well.

FIG. 5 is an exemplary flowchart for an integrated optical metrologyprocess of using a profile model for determining one or more features ofmeasurement structures fabricated for consecutive measurement and usingthe one or more determined features for automated process control. Instep 800, based on the type of optical metrology tool to be used, thenumber of profile models is determined. In particular, as mentionedabove, when a polarized reflectometer is used, one profile model is usedfor examining both structures 504 and 508.

In the present exemplary embodiment, in step 805, the profile modelsdetermined in step 800 are optimized. In some applications, step 805 canbe omitted. Thus, the following steps can be performed using the profilemodel without optimizing it. For a detailed description of modeling ofone-dimension repeating structures, refer to U.S. patent applicationSer. No. 10/206,291, OPTIMIZED MODEL AND PARAMETER SELECTION FOR OPTICALMETROLOGY, by Vuong, et al., filed on Jun. 27, 2002, and is incorporatedin its entirety herein by reference. For a detailed description ofmodeling two-dimension repeating structures, refer to U.S. patentapplication Ser. No. 11/061,303, OPTICAL METROLOGY OPTIMIZATION FORREPETITIVE STRUCTURES, by Vuong, et al., filed on Apr. 27, 2004, and isincorporated in its entirety herein by reference.

In step 810, if a metrology data store is determined to be desired bythe application, a metrology data store is generated for each profilemodel. Typically, a metrology data store is desired if measurement ofthe structure is done using an integrated metrology device in a waferfabrication cluster unit such as a track or etcher. A metrology datastore is typically not desired if determination of the one or morefeatures of the measurement structures is done in real time or is doneutilizing the regression method mentioned above. If a metrology datastore is not desired by the application, then processing proceeds tostep 880. Otherwise, in step 820, one or more metrology data stores aregenerated for each profile model.

Typically, the metrology data store comprises a table, a library or atrained machine learning system (MLS). A table or a library includespairs of simulated diffraction signal and associated profile parameters.For a more detailed description of a library-based process, see U.S.Pat. No. 6,943,900, titled GENERATION OF A LIBRARY OF PERIODIC GRATINGDIFFRACTION SIGNALS, filed on Jul. 16, 2001, issued on Sep. 13, 2005,which is incorporated herein by reference in its entirety. A trained MLSis created to generate a profile or a set of profile parameters based oninput measured diffraction signal. For a more detailed description of agenerating and using a trained MLS, see U.S. patent application Ser. No.10/608,300, titled OPTICAL METROLOGY OF STRUCTURES FORMED ONSEMICONDUCTOR WAFERS USING MACHINE LEARNING SYSTEMS, filed on Jun. 27,2003, which is incorporated herein by reference in its entirety.

In the present exemplary embodiment, independent of steps 800 to 820, instep 860, one or more pairs of measurement structures are fabricated forconsecutive measurement. Typical uses of consecutive measurementsinclude measurement of structures to determine the astigmatism error, inchemical-mechanical planarization (CMP) where repeating structures maybe in different layers such that a structure in a first layer is rotatedninety degrees compared to a second structure in another layer.Astigmatism error is the difference between a value of a feature withthe repeating structure in a first orientation relative to theillumination beam and the value of the same feature of the repeatingstructure at a second orientation relative to the illumination beam. Forexample, the critical dimension (CD) of a line and space repeatingstructure with the line in the horizontal position and the CD of thesame repeating structure with the line in a vertical position, similarto the layout if the initial layout was rotated 90 degrees.

Still referring to FIG. 5, in step 865, the diffraction signal off thefirst measurement structure of the first pair of measurement structuresis obtained. For example, if there are two pairs of measurementstructures such as those depicted in FIG. 4C, any one of the measurementstructures 542, 544, 546 or 548 may be designated as the firstmeasurement structure and measured first. In step 870, the diffractionsignals off the remaining measurement structures are obtained. Using theabove example of the measurement structures depicted in FIG. 4C, ifmeasurement structure 542 was measured first, then the remainingstructures, 544, 546, and 548, are measured consecutively.

In the present exemplary embodiment, referring to FIG. 5, in step 875,preliminary processing of the obtained diffraction signals is performed.As mentioned above, when a polarized reflectometer is used, one of themeasured diffraction signal is converted to a converted diffractionsignal. In particular, a converted diffraction signal for one measureddiffraction signal is calculated as the negative of another measureddiffraction signal.

Preliminary processing also includes selecting the diffractions signalsat wavelengths that will be used in the regression method or thewavelengths used to create the metrology data stores. For example, alibrary of pairs of profile parameters and simulated diffraction signalsmay have been generated using only selected wavelengths based onprevious experience with the application or prior testing of the profilemodel while generating the metrology data store. Furthermore, a trainedMLS may also have been trained only on data that were obtained based onselected wavelengths. Additional processing of the obtained diffractionsignals include signal filtering based on weighting functions as afunction of noise in the measured signal, accuracy of the measuredsignal, and sensitivity of the measured signal. For a more detaileddescription of applying weighting functions to enhance measureddiffraction signals, see U.S. patent application Ser. No. 11/371,752,titled WEIGHTING FUNCTION TO ENHANCE MEASURED DIFFRACTION SIGNALS INOPTICAL METROLOGY, filed on Mar. 8, 2006, which is incorporated hereinby reference in its entirety.

In step 880, one or more features of the measurement structures aredetermined using a metrology data store or regression. When a metrologydata store is used, a best match simulated diffraction signal to theprocessed diffraction signal obtained from step 875 is determined andthe associated profile or set of profile parameters from the table orlibrary is selected. When a trained MLS is used, the processeddiffraction signal is input to the trained MLS to generate a profile ora set of profile parameters. As mentioned above, the profile obtainedfrom either the table, library, or trained MLS is presumed to be thesame as the profile of the metrology target structure. When theregression method is used, the processed measured diffraction signal offthe measurement structure is compared to a simulated diffraction signalgenerated using a hypothetical profile. The process is iterated if thediffraction signals do not match within preset or matching criteria. Fora more detailed description of a regression-based process, see U.S. Pat.No. 6,785,638, titled METHOD AND SYSTEM OF DYNAMIC LEARNING THROUGH AREGRESSION-BASED LIBRARY GENERATION PROCESS, issued on Aug. 31, 2004,which is incorporated herein by reference in its entirety.

In step 885, data on the determined features of the measurementstructures are transmitted to the-current, previous, or laterfabrication process device. For example, critical dimensions of themeasurement structures determined at an etch fabrication cluster aretransmitted to the controller of the current etch fabrication cluster,to a previous fabrication device such as a photolithography cluster, orto a later fabrication device such as a deposition cluster. In step 890,at least one process variable in the current, previous, or laterfabrication cluster is adjusted based on the transmitted data on thedetermined features of the measurement structures.

FIG. 6 is an exemplary architectural diagram for fabrication clustersystems linked with a metrology processor for determining features ofwafer structures and using the features for advanced process control. Afirst fabrication system 940 includes a model optimizer 942, a real timeprofile estimator 944, diffraction signal processor 946, a fabricationcluster 948, and a metrology cluster 950. The first fabrication system940 is coupled to a metrology processor 1010. The metrology processor1010 is coupled to metrology data sources 1000, a metrology data store1040, and the fabrication host processors 1020. The model optimizer 942contains the logic to optimize a profile model of a measurementstructure. The real time profile estimator 944 has the logic todetermine the best match profile for a measured diffraction signal usingregression. The diffraction signal processor 946 utilizes a metrologydata store 1040 associated with a measurement structure to determine thebest match profile for a measured diffraction signal, and the like. Thefabrication cluster 948 may be a track, etcher, deposition process tool.The metrology cluster 950 comprises a set of metrology tools such aspolarized reflectometers, spectroscopic ellipsometers, and the like. Thesecond fabrication system 970 includes a model optimizer 972, a realtime profile estimator 974, diffraction signal processor 976, afabrication cluster 978, and a metrology cluster 980 and these deviceshave the same functions as the equivalent devices in the firstfabrication system 940. The first and second fabrication systems, 940and 970, are coupled to metrology processor 1010.

Referring to FIG. 6, the metrology processor 1010 receives metrologydata 864 from the offline or remote metrology data sources 1000. Theoffline metrology data sources 1000 may be an offline cluster ofmetrology devices in the fabrication site such as reflectometers,ellipsometers, SEM's and the like. The remote metrology data sources1000 may include a remote data server or remote processor or websitethat provides metrology data for the application. Data 860 from thefirst fabrication system 940 to the metrology processor 1010 may includethe profile parameter ranges of the profile model and the generated datastores to determine the structure features. The data stores 1040 mayinclude a library of pairs of simulated diffraction signals andcorresponding sets of profile parameters or a trained MLS system thatcan generate a set of profile parameters for an input measureddiffraction signal. Data 870 from data stores 1040 to metrologyprocessor 1010 includes a set of profile parameters and/or simulateddiffraction signal. Data 864 from the metrology processor 1010 tometrology data store 1040 includes values of the profile parameters,material refraction parameters, and metrology device parameters in orderto specify the portion of the data space to be searched in the libraryor trained MLS store in the metrology data store 1040. Data 862transmitted to and from the second fabrication system 970 to themetrology processor 1010 are similar to the data 860 transmitted to andfrom the first fabrication system 940.

Still referring to FIG. 6, data 866 transmitted to and from themetrology processor 1010 to the fabrication host processor 1020 mayinclude data related to the application recipe and process data measuredby the metrology clusters 950 and 980, in the first and secondfabrication systems 940 and 970. The metrology data store 1040 in FIG. 6is the repository of metrology data and the metrology data is madeavailable to the first and/or the second fabrication system 940 and 970.As mentioned above, the first and/or second fabrication system 940 and970 may include one or more of a photolithography, etch, thermalprocessing system, metallization, implant, chemical vapor deposition,chemical mechanical polishing, or other fabrication unit.

Data on the features of the measurement structures determined by thereal time profile estimator 944 or diffraction signal processor 946 inthe first fabrication system 940 may be transmitted to the fabricationhost processor 1020. The data can be used by the fabrication hostprocessor to adjust a process variable in the fabrication cluster 948 ofthe first fabrication system 940 or adjust a process variable in thefabrication cluster 978 of the second fabrication system 970. Forexample, if the fabrication cluster 948 is a photolithography unit andthe fabrication cluster 978 is an etch unit, the data may be topcritical dimension of a measurement structure measured by the metrologycluster 950. The value of the top critical dimension may be used by thefabrication host processor 1020 to adjust the focus or exposure of thephotolithography unit. Furthermore, the value of the top criticaldimension may be used by the fabrication host processor 1020 to adjustan etch variable such as flow rate of the etchant. In a similar manner,the value of a profile parameter of a measurement structure measured bythe metrology cluster 980 and determined by the real time profileestimator 974 or the profile server 976 of the second fabrication system970 may be transmitted to the fabrication host processor 1020. The valueof the profile parameter can be used by the fabrication host processorto adjust a process variable in the fabrication cluster 948 of the firstfabrication system 940 or adjust a process variable in the fabricationcluster 978 of the second fabrication system 970. It is understood thatthe second fabrication system may include any fabrication clusterinvolved in the wafer manufacturing process.

In particular, it is contemplated that functional implementation of thepresent invention described herein may be implemented equivalently inhardware, software, firmware, and/or other available functionalcomponents or building blocks. For example, the metrology data store maybe in computer memory or in an actual computer storage device or medium.Other variations and embodiments are possible in light of aboveteachings, and it is thus intended that the scope of invention not belimited by this Detailed Description, but rather by Claims following.

1. A method of examining structures formed on a semiconductor waferusing consecutive measurements, the method comprising; forming a firststructure on a semiconductor wafer; forming a second structure abuttingthe first structure; measuring a first measured diffraction signal ofthe first structure using a polarized reflectometer; measuring a secondmeasured diffraction signal of the second structure using the polarizedreflectometer, wherein the first and second measured diffraction signalsare measured consecutively; comparing the first measured diffractionsignal to a first simulated diffraction signal generated using a profilemodel of the first structure, the profile model having profileparameters that characterize geometries of the first structure;determining one or more features of the first structure based on thecomparison of the first measured diffraction signal to the firstsimulated diffraction signal; converting the second measured diffractionsignal to a converted diffraction signal; comparing the converteddiffraction signal to the first simulated diffraction signal or a secondsimulated diffraction signal generated using the same profile model asthe first simulated diffraction signal; and determining one or morefeatures of the second structure based on the comparison of theconverted diffraction signal to the first or second simulateddiffraction signal.
 2. The method of claim 1, wherein converting thesecond measured diffraction signal comprises calculating a negative ofthe second measured diffraction signal.
 3. The method of claim 1,wherein the second structure is formed to have the same features as thefirst structure rotated about 90 degrees.
 4. The method of claim 3,wherein the first and second structures are repeating line and spacestructures, wherein the lines of the first structure are oriented atabout 90 degrees relative to the lines of the second structure.
 5. Themethod of claim 3, wherein the first and second structures have profilesthat vary in two dimensions and characterized using unit cells, whereinthe unit cell of the first structure is oriented at about 90 degreesrelative to the unit cell of the second structure.
 6. The method ofclaim 1, wherein the second measured diffraction signal is measuredwithout unloading and reloading the semiconductor wafer after the firstmeasured diffraction signal is measured.
 7. The method of claim 6,wherein the second measured diffraction signal is measured withoutmeasuring another diffraction signal of another structure after thefirst measured diffraction signal is measured.
 8. The method of claim 1,further comprising: obtaining the first simulated diffraction signalfrom a library of simulated diffraction signals before comparing thefirst measured diffraction signal to the first simulated diffractionsignal; and obtaining the second simulated diffraction signal from thelibrary of simulated diffraction signals before comparing the converteddiffraction signal to the second simulated diffraction signal, whereinthe simulated diffraction signals in the library of simulateddiffraction signals were generated using one profile model for the firstand second structures.
 9. The method of claim 8, wherein profileparameters of the one profile model for the first and second structureswere varied to generate a set of hypothetical profiles, and wherein thesimulated diffraction signals in the library of simulated diffractionsignals were generated using the set of hypothetical profiles.
 10. Themethod of claim 1, wherein comparing the converted diffraction signalcomprises: comparing the converted diffraction signal to the firstsimulated diffraction signal; if the converted diffraction signal andthe first simulated diffraction signal do not match within a matchingcriterion: generating a hypothetical profile by adjusting one or moreprofile parameters of the profile model; and generating the secondsimulated diffraction signal using the hypothetical profile.
 11. Themethod of claim 1 further comprising: transmitting data on the one ofmore determined features of the first or second structure to a metrologyprocessor.
 12. The method of claim 11, wherein the transmitted data isused to alter at least one process variable in fabrication device.
 13. Amethod of examining structures formed on a semiconductor wafer usingconsecutive measurements, the method comprising; obtaining a firstmeasured diffraction signal of a first structure formed on thesemiconductor wafer, the first measured diffraction signal measuredusing a polarized reflectometer; obtaining a second diffraction signalof a second structure formed abutting the first structure, the secondmeasured diffraction signal measured using the polarized reflectometer,wherein the first and second measured diffraction signals are measuredconsecutively; comparing the first measured diffraction signal to afirst simulated diffraction signal generated using a profile model ofthe first structure, the profile model having profile parameters thatcharacterize geometries of the first structure; determining one or morefeatures of the first structure based on the comparison of the firstmeasured diffraction signal to the first simulated diffraction signal;converting the second measured diffraction signal to a converteddiffraction signal; comparing the converted diffraction signal to thefirst simulated diffraction signal or a second simulated diffractionsignal generated using the same profile model as the first simulateddiffraction signal; and determining one or more features of the secondstructure based on the comparison of the converted diffraction signal tothe first or second simulated diffraction signal.
 14. The method ofclaim 13, wherein converting the second measured diffraction signalcomprises calculating a negative of the second measured diffractionsignal.
 15. The method of claim 13, further comprising: obtaining thefirst simulated diffraction signal from a library of simulateddiffraction signals before comparing the first measured diffractionsignal to the first simulated diffraction signal; and obtaining thesecond simulated diffraction signal from the library of simulateddiffraction signals before comparing the converted diffraction signal tothe second simulated diffraction signal, wherein the simulateddiffraction signals in the library of simulated diffraction signals weregenerated using one profile model for the first and second structures.16. The method of claim 15, wherein profile parameters of the oneprofile model for the first and second structures were varied togenerate a set of hypothetical profiles, and wherein the simulateddiffraction signals in the library of simulated diffraction signals weregenerated using the set of hypothetical profiles.
 17. The method ofclaim 13, wherein comparing the converted diffraction signal comprises:comparing the converted diffraction signal to the first simulateddiffraction signal; if the converted diffraction signal and the firstsimulated diffraction signal do not match within a matching criterion:generating a hypothetical profile by adjusting one or more profileparameters of the profile model; and generating the second simulateddiffraction signal using the hypothetical profile.
 18. The method ofclaim 13 further comprising: transmitting data on the one of moredetermined features of the first or second structure to a metrologyprocessor.
 19. The method of claim 18, wherein the transmitted data isused to alter at least one process variable in fabrication device.
 20. Acomputer-readable storage medium having computer-executable instructionsfor examining structures formed on a semiconductor wafer usingconsecutive measurements, comprising instructions for; obtaining a firstmeasured diffraction signal of a first structure formed on thesemiconductor wafer, the first measured diffraction signal measuredusing a polarized reflectometer; obtaining a second diffraction signalof a second structure formed abutting the first structure, the secondmeasured diffraction signal measured using the polarized reflectometer,wherein the first and second measured diffraction signals are measuredconsecutively; comparing the first measured diffraction signal to afirst simulated diffraction signal generated using a profile model ofthe first structure, the profile model having profile parameters thatcharacterize geometries of the first structure; determining one or morefeatures of the first structure based on the comparison of the firstmeasured diffraction signal to the first simulated diffraction signal;converting the second measured diffraction signal to a converteddiffraction signal; comparing the converted diffraction signal to thefirst simulated diffraction signal or a second simulated diffractionsignal generated using the same profile model as the first simulateddiffraction signal; and determining one or more features of the secondstructure based on the comparison of the converted diffraction signal tothe first or second simulated diffraction signal.
 21. Thecomputer-readable storage medium of claim 20, wherein converting thesecond measured diffraction signal comprises calculating a negative ofthe second measured diffraction signal.
 22. A system to examinestructures formed on a semiconductor wafer using consecutivemeasurements, the system comprising; a polarized reflectometerconfigured to: measure a first diffraction signal of a first structureformed on the semiconductor wafer; and measure a second diffractionsignal of a second structure formed abutting the first structure,wherein the first and second measured diffraction signals are measuredconsecutively; and a metrology processor configured to: obtain the firstmeasured diffraction signal; compare the first measured diffractionsignal to a first simulated diffraction signal generated using a profilemodel of the first structure, the profile model having profileparameters that characterize geometries of the first structure;determine one or more features of the first structure based on thecomparison of the first measured diffraction signal to the firstsimulated diffraction signal; obtain the second measured diffractionsignal; convert the second measured diffraction signal to a converteddiffraction signal; compare the converted diffraction signal to thefirst simulated diffraction signal or a second simulated diffractionsignal generated using the same profile model as the first simulateddiffraction signal; and determine one or more features of the secondstructure based on the comparison of the converted diffraction signal tothe first or second simulated diffraction signal.
 23. The system ofclaim 22, further comprising: a fabrication cluster configured to formthe first and second structures on the semiconductor wafer.
 24. Thesystem of claim 23, wherein the metrology processor is furtherconfigured to: transmit data on the one of more determined features ofthe first or second structure to the current, previous, or laterfabrication cluster.
 25. The system of claim 24, wherein the transmitteddata is used to alter a process variable in the current, previous, orlater fabrication clusters.
 26. The system of claim 22, whereinconverting the second measured diffraction signal comprises calculatinga negative of the second measured diffraction signal.